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About this book
About this book
Spatial uncertainty analysis has become a recognized discipline that integrates expertise from geographic information science, remote sensing, spatial and classical statistics and many others. The chapters are divided into two sections; the first section concentrates on accuracy assessment issues and the second on modelling uncertainty.This book will be useful both to those new to spatial uncertainty assessment and to experienced practitioners. Those interested in the application of appropriate uncertainty assessment techniques are provided with examples of many applications based in remote sensing and geographic information systems (GIS). For researchers, this book presents a snapshot of the state-of-the-art of uncertainty assessment, providing theoretical chapters based in classical and spatial statistics.
Introduction: The Past, Present, and Future of Uncertainty Analysis. Section I. Accuracy Assessment Issues. Chapter 1: Communicating the Results of Accuracy Assessment: Metadata, Digital Libraries, and Assessing Fitness for Use. Chapter 2: From Data Accuracy to Data Quality: Using Spatial Statistics to Predict the Implications of Spatial Error in Point Data. Chapter 3: Generalized Linear Mixed Models for Analyzing Error in a Satellite-based Vegetation Map of Utah. Chapter 4: Increasing Spatial Precision and Accuracy for Monitoring Peatlands in Switzerland by Remote Sensing Techniques. Chapter 5: The Effect of Uncertain Locations on Disease Cluster Statistics. Chapter 6: Double Sampling for Area Estimation and Map Accuracy Assessment. Chapter 7: Areal Estimates and Accuracy Assessments Using Two-phase Stratified Random Sampling, Cluster Plots and the Multivariate Composite Estimator. Section II. Modeling Spatial and Temporal Uncertainty. Chapter 8: Acquisition of Spatial Data by Forestry Management Agencies. Chapter 9: Choosing Between Abrupt and Gradual Spatial Variation? Chapter 10: Modeling the Spatial Distribution of 10 Tree Species in Pennsylvania. Chapter 11: Realistic Spatial Models: The Accurate Mapping of Environmental Factors based on Synecological Coordinates. Chapter 12: Discrete Polygons or a Continuous Surface: Which is the Appropriate Way to Model Forests Cartographically. Chapter 13: Statistical Models of Landscape Pattern and the Effects of Coarse Spatial Resolution on Estimation of Area with Satellite Imagery. Chapter 14: The Simultaneous Nature of Tree Growth Models. Chapter 15: Incorporating Soil Variability into a Spatially Distributed Model of Percolate Accounting. Chapter 16: Analyzing Spatiotemporal Dynamics of Plant Populations and Vegetation. Chapter 17: Space-Time Statistical Modeling of Environmental Data. Analysis. Section I. Accuracy Assessment Issues. Section II. Modeling Spatial and Temporal Uncertainty.